{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#数据集 http://archive.ics.uci.edu/ml/machine-learning-databases/adult/\n",
    "import os\n",
    "import pandas as pd\n",
    "data_folder = os.path.join(os.path.expanduser(\"~\"), \"data\", \"datasets\")\n",
    "adult_filename = os.path.join(data_folder, \"adult.data\")\n",
    "adult_filename = \"adult.data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adult = pd.read_csv(adult_filename, header=None, names=[\"Age\", \"Work-Class\", \"fnlwgt\", \"Education\",\n",
    "                                                        \"Education-Num\", \"Marital-Status\", \"Occupation\",\n",
    "                                                        \"Relationship\", \"Race\", \"Sex\", \"Capital-gain\",\n",
    "                                                        \"Capital-loss\", \"Hours-per-week\", \"Native-Country\",\n",
    "                                                        \"Earnings-Raw\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adult.dropna(how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   Age         Work-Class  fnlwgt   Education  Education-Num  \\\n0   39          State-gov   77516   Bachelors             13   \n1   50   Self-emp-not-inc   83311   Bachelors             13   \n2   38            Private  215646     HS-grad              9   \n3   53            Private  234721        11th              7   \n4   28            Private  338409   Bachelors             13   \n\n        Marital-Status          Occupation    Relationship    Race      Sex  \\\n0        Never-married        Adm-clerical   Not-in-family   White     Male   \n1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n\n   Capital-gain  Capital-loss  Hours-per-week  Native-Country Earnings-Raw  \n0          2174             0              40   United-States        <=50K  \n1             0             0              13   United-States        <=50K  \n2             0             0              40   United-States        <=50K  \n3             0             0              40   United-States        <=50K  \n4             0             0              40            Cuba        <=50K  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Work-Class</th>\n      <th>fnlwgt</th>\n      <th>Education</th>\n      <th>Education-Num</th>\n      <th>Marital-Status</th>\n      <th>Occupation</th>\n      <th>Relationship</th>\n      <th>Race</th>\n      <th>Sex</th>\n      <th>Capital-gain</th>\n      <th>Capital-loss</th>\n      <th>Hours-per-week</th>\n      <th>Native-Country</th>\n      <th>Earnings-Raw</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>77516</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>83311</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>215646</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>53</td>\n      <td>Private</td>\n      <td>234721</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>28</td>\n      <td>Private</td>\n      <td>338409</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['Age', 'Work-Class', 'fnlwgt', 'Education', 'Education-Num',\n       'Marital-Status', 'Occupation', 'Relationship', 'Race', 'Sex',\n       'Capital-gain', 'Capital-loss', 'Hours-per-week', 'Native-Country',\n       'Earnings-Raw'],\n      dtype='object')"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "count    32561.000000\nmean        40.437456\nstd         12.347429\nmin          1.000000\n25%         40.000000\n50%         40.000000\n75%         45.000000\nmax         99.000000\nName: Hours-per-week, dtype: float64"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Hours-per-week\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "10.0"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Education-Num\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\n       ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay',\n       ' Never-worked'], dtype=object)"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Work-Class\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 20.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 31.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 28.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 77.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 69.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 55.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 70.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 47.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 12.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 27.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='Education-Num', ylabel='Hours-per-week'>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x648 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(12, 9))\n",
    "sns.swarmplot(x=\"Education-Num\", y=\"Hours-per-week\", hue=\"Earnings-Raw\", data=adult[::50], size=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.arange(30).reshape((10, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0,  1,  2],\n       [ 3,  4,  5],\n       [ 6,  7,  8],\n       [ 9, 10, 11],\n       [12, 13, 14],\n       [15, 16, 17],\n       [18, 19, 20],\n       [21, 22, 23],\n       [24, 25, 26],\n       [27, 28, 29]])"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X[:,1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0,  1,  2],\n       [ 3,  1,  5],\n       [ 6,  1,  8],\n       [ 9,  1, 11],\n       [12,  1, 14],\n       [15,  1, 17],\n       [18,  1, 20],\n       [21,  1, 23],\n       [24,  1, 26],\n       [27,  1, 29]])"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vt = VarianceThreshold()\n",
    "Xt = vt.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0,  2],\n       [ 3,  5],\n       [ 6,  8],\n       [ 9, 11],\n       [12, 14],\n       [15, 17],\n       [18, 20],\n       [21, 23],\n       [24, 26],\n       [27, 29]])"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27.  0. 27.]\n"
     ]
    }
   ],
   "source": [
    "print(vt.variances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = adult[[\"Age\", \"Education-Num\", \"Capital-gain\", \"Capital-loss\", \"Hours-per-week\"]].values\n",
    "y = (adult[\"Earnings-Raw\"] == ' >50K').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "transformer = SelectKBest(score_func=chi2, k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.60061182e+03 2.40142178e+03 8.21924671e+07 1.37214589e+06\n",
      " 6.47640900e+03]\n"
     ]
    }
   ],
   "source": [
    "Xt_chi2 = transformer.fit_transform(X, y)\n",
    "print(transformer.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "def multivariate_pearsonr(X, y):\n",
    "    scores, pvalues = [], []\n",
    "    for column in range(X.shape[1]):\n",
    "        cur_score, cur_p = pearsonr(X[:,column], y)\n",
    "        scores.append(abs(cur_score))\n",
    "        pvalues.append(cur_p)\n",
    "    return (np.array(scores), np.array(pvalues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2340371  0.33515395 0.22332882 0.15052631 0.22968907]\n"
     ]
    }
   ],
   "source": [
    "transformer = SelectKBest(score_func=multivariate_pearsonr, k=3)\n",
    "Xt_pearson = transformer.fit_transform(X, y)\n",
    "print(transformer.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "#from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.model_selection  import cross_val_score\n",
    "clf = DecisionTreeClassifier(random_state=14)\n",
    "scores_chi2 = cross_val_score(clf, Xt_chi2, y, scoring='accuracy')\n",
    "scores_pearson = cross_val_score(clf, Xt_pearson, y, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi2 performance: 0.829\n",
      "Pearson performance: 0.772\n"
     ]
    }
   ],
   "source": [
    "print(\"Chi2 performance: {0:.3f}\".format(scores_chi2.mean()))\n",
    "print(\"Pearson performance: {0:.3f}\".format(scores_pearson.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.utils import as_float_array\n",
    "\n",
    "class MeanDiscrete(TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        X = as_float_array(X)\n",
    "        self.mean = np.mean(X, axis=0)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X = as_float_array(X)\n",
    "        assert X.shape[1] == self.mean.shape[0]\n",
    "        return X > self.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_discrete = MeanDiscrete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_mean = mean_discrete.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "from numpy.testing import assert_array_equal\n",
    "\n",
    "def test_meandiscrete():\n",
    "    X_test = np.array([[ 0,  2],\n",
    "                        [ 3,  5],\n",
    "                        [ 6,  8],\n",
    "                        [ 9, 11],\n",
    "                        [12, 14],\n",
    "                        [15, 17],\n",
    "                        [18, 20],\n",
    "                        [21, 23],\n",
    "                        [24, 26],\n",
    "                        [27, 29]])\n",
    "    mean_discrete = MeanDiscrete()\n",
    "    mean_discrete.fit(X_test)\n",
    "    assert_array_equal(mean_discrete.mean, np.array([13.5, 15.5]))\n",
    "    X_transformed = mean_discrete.transform(X_test)\n",
    "    X_expected = np.array([[ 0,  0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1]])\n",
    "    assert_array_equal(X_transformed, X_expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "test_meandiscrete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([('mean_discrete', MeanDiscrete()),\n",
    "                     ('classifier', DecisionTreeClassifier(random_state=14))])\n",
    "scores_mean_discrete = cross_val_score(pipeline, X, y, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Discrete performance: 0.803\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Discrete performance: {0:.3f}\".format(scores_mean_discrete.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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